Machine Learning Application Engineer

Key Skills

BERT, GPT, Large Language Models - LLMs, Machine learning techniques, Pandas, Python Programming, PyTorch, R Programming, Scikit-learn, TensorFlow

Job Description

As a Software Engineer you will be responsible in building the next generation of intelligent systems for Apple’s corporate applications. You will work in a dynamic environment to take an ideation to implementation in a multi-functional environment. The ideal candidate will be self-motivated, pro-active and solution oriented. Attention to detail and dedication to providing a high-quality, stable delivery are essential.

You will be challenged to find creative solutions to technical problems, feel comfortable working with sophisticated systems and large data sets, collaborating in a team environment. We promote innovation to improve our product performance and stay deeply focused on delighting our users.

Minimum Qualifications

• Bachelor’s degree in Computer Science or equivalent work experience.
• 4+ years of professional experience as a Software Engineer in AI or ML Platforms
• 4+ years of professional experience API development for serving generative models using Java, Springboot framework
• Prior experience of working with machine learning & LLMs
• 4+ years of professional experience in developing data pipelines and working with streaming data processing frameworks like Apache Kafka, Flink, or Spark.

Preferred Qualifications

• Proficiency in programming languages such as Java, Python or R
• Knowledge of data engineering principles, including data preprocessing, ETL processes, and tools
• Proven track record of working on AI/ML projects from conception to deployment.
• Prior experience with prompt engineering and vector embeddings.
• Knowledge of LLM model architectures - GPT, BERT
• Experience collaborating with ML Platform team utilising and integrating ML APIs with applications
• Familiarity with ML libraries and frameworks, including TensorFlow, PyTorch, Pandas, and Scikit-learn.
• Understanding of RAG and graph-based RAG methodologies.
• Skills in rapid prototyping and experimentation with generative models
• Experience in performance optimisation, particularly in enhancing model performance and inference speed for real-time applications+ Understanding of RAG and graph-based RAG methodologies.
• Skills in rapid prototyping and experimentation with generative models
• Experience in performance optimisation, particularly in enhancing model performance and inference speed for real-time applications